Safe Harbors creates clear distinctions between protected data mining by AI and unprotected copyright infringement which is the biggest issue hampering further AI development and innovation

Lessons can be learned from the implementation of TDM exceptions in other jurisdictions to craft a safe harbor for data mining in the United States. Such a safe harbor should (1) define “data mining” broadly; (2) clearly allow reproductions, derivations, and dataset-sharing for data mining purposes; (3) allow non-commercial and commercial uses to the same extent; and (4) be limited to functional, non-expressive uses of data mining. A safe harbor should broadly define “data mining” to cover a wide range of pattern extraction techniques, not limited to AI/ML. One potential definition is proposed by Jean-Paul Triaille: “The automated processing of digital materials, which may include texts, data, sounds, images or other elements, or a combination of these, in order to uncover new knowledge or insights” (although he uses the term “data analysis” instead of “data mining”).192 While other jurisdictions refer to “text and data mining,” “text” is redundant, as it is encapsulated in “data.”193 In crafting the safe harbor, what matters is not the exact technique through which patterns are extracted, but the automated process of gaining functional insights from expressive data. While this Note specifically considers data mining in a machine learning and artificial intelligence context, using a broad definition of data mining in a copyright safe harbor would not foreclose future technologies that employ other techniques for pattern extraction. A safe harbor should also be specific enough in the rights that are conferred to data miners to provide legal clarity. Given the intimidating U.S. copyright remedial regime and the unpredictable fair use doctrine, smaller innovators in the United States would benefit from an exception that clearly spells out allowed uses.194 Here, lessons can be drawn from Japan’s recent amendments to clarify its copyright exception, which provide an innovation-friendly and clear implementation of an exception.195 It should be clearly enumerated that reproductions and derivations (for labeling and annotation) of works are permitted in a data mining context. Further, the secure storage, retention, and sharing of datasets should also be permitted for verification purposes. Allowing researchers and regulators the ability to scrutinize how data is being used would promote fairer, safer, and higher-quality AI products.196 Policymakers should also be careful to not impose limitations on commercial uses, such as opt-out mechanisms, that could unduly stifle innovation contrary to the expressed policy of copyright law in the United States.197 Unlike the exceptions of the United Kingdom and European Union, commercial uses should be permitted to the same extent that research and non-profit uses are allowed. The U.S. fair use doctrine already includes a copyright exception for research uses and considers nonprofit uses favorably.198 Thus, the proposed safe harbor would need to specifically free up commercial uses of data mining. Rightsholders should not be allowed to opt-out of uses of their works for commercial data mining purposes because doing so would legitimize a licensing market for machine learning training data that could prompt further legal confusion, put smaller innovators at a competitive disadvantage, and lead to lower quality, biased AI systems.199 Finally, and most importantly, the safe harbor should be limited to functional, non-expressive uses of data mining. Some emerging AI technologies use expressive data to generate expressive works and are not covered within the scope of this Note.200 Because expressive AI-generated works could compete with and effectively replace original works, it would be more difficult to justify a categorical safe harbor for these works without unduly encumbering authors and creators. Instead, the fair use doctrine should be applied on a caseby-case basis for expressive uses of data mining. In sum, a data mining safe harbor should (1) define “data mining” broadly; (2) clearly allow reproductions, derivations, and dataset-sharing for data mining purposes; (3) allow non-commercial and commercial uses to the same extent; and (4) be limited to functional, non-expressive uses of data mining. As the United States strives to maintain its dominance in artificial intelligence,201 it must consider how existing laws enable or stifle technological progress. Copyright law presents a potential barrier for AI growth when machine learning models are trained using expressive data. Fundamentally, data mining is not copyright infringement. While the fair use doctrine provides a degree of flexibility in U.S. copyright law, certainty in the law is desirable for smaller actors. Practically, the fair use doctrine is unpredictable and has been stretched beyond its limits in new technological contexts. Establishing a clear right to use copyrighted materials to train functional machine learning models is consistent with the goals of copyright law and would ultimately remove barriers to innovation. Rather than rely on courts to rule on important issues of technology policy, Congress should be more proactive in enacting a data mining safe harbor. A safe harbor would make legislatively clear what should already be clear given the scope of copyright law: data mining is not infringement at all.